Investor Relations: A Study of Perceived Fundamental Skills and Practices in Corporations and Agencies

Today, the importance of investor relations as a strategic management function in publicly held companies is growing. In the interdisciplinary field of investor relations, professionals need both finance and communication expertise to achieve effective investor relations results. This paper investigates investor relations professionals\u27 perceptions of the fundamental skills and practices needed in corporations and agencies through an online self-administered survey. It finds that the professionals perceived relationship-building skills with media and the investment community to be the most important. A knowledge of information disclosure process, senior management assistance and crisis communication management were also seen as important. The professionals indicated that improving skills and knowledge of communications and finance was as important as the skills themselves. This study also finds that professionals with different affiliations and levels of experience have different perceptions of the fundamental skills and practices in the field of investor relations

Markov Chain Monte Carlo Sampling on Polar Sea Ice Classification

The Remund-Long (RL) Multi-Sensor Sea Ice Classification algorithm� combines both radiometer and scatterometer data using Principle Component Analysis and reduces the dimensionality and noise level of the data. The algorithm uses an iterative Maximum a Posteriori (MAP) method based on a multi-variant Gaussian model with a temporal prior. As a result, the algorithm successfully classifies Winter Antarctic region into five different ice types. However, due to the nature of this pixel wise classification algorithm, the final classification is more likely to be corrupted by slat-pepper-shaped artifacts. Such artifacts are introduced by the Scatterometer Image Reconstruction (SIR) algorithm which utilizes multi-swath raw scatterometer data to generate high resolution images. In order to resolve such problem in RL algorithm, posterior distribution function with spatial prior is embedded into the classification process. A Markov Chain Monte Carlo (MCMC) sampling method is one way to sample such posterior distribution of the state space in which each element of the space has the size of an entire image. This report gives a brief introduction to the concept of Metropolis-Hastings Markov Chain Monte Carlo (MH MCMC) algorithm, discusses its implementation on polar sea ice classification, and compares the result with the RL algorithm

Spatiotemporal Besov Priors for Bayesian Inverse Problems

Fast development in science and technology has driven the need for proper statistical tools to capture special data features such as abrupt changes or sharp contrast. Many applications in the data science seek spatiotemporal reconstruction from a sequence of time-dependent objects with discontinuity or singularity, e.g. dynamic computerized tomography (CT) images with edges. Traditional methods based on Gaussian processes (GP) may not provide satisfactory solutions since they tend to offer over-smooth prior candidates. Recently, Besov process (BP) defined by wavelet expansions with random coefficients has been proposed as a more appropriate prior for this type of Bayesian inverse problems. While BP outperforms GP in imaging analysis to produce edge-preserving reconstructions, it does not automatically incorporate temporal correlation inherited in the dynamically changing images. In this paper, we generalize BP to the spatiotemporal domain (STBP) by replacing the random coefficients in the series expansion with stochastic time functions following Q-exponential process which governs the temporal correlation strength. Mathematical and statistical properties about STBP are carefully studied. A white-noise representation of STBP is also proposed to facilitate the point estimation through maximum a posterior (MAP) and the uncertainty quantification (UQ) by posterior sampling. Two limited-angle CT reconstruction examples and a highly non-linear inverse problem involving Navier-Stokes equation are used to demonstrate the advantage of the proposed STBP in preserving spatial features while accounting for temporal changes compared with the classic STGP and a time-uncorrelated approach.Comment: 29 pages, 13 figure

Magnons, Phonons, and Thermal Hall Effect in Candidate Kitaev Magnet α\alpha-RuCl3_3

We study the nature of the debated thermal Hall effect in the candidate Kitaev material $\alpha$-RuCl$_3$. Without assuming the existence of a gapped spin liquid, we show that a realistic minimal spin model in the canted zigzag phase suffices, at the level of linear spin-wave theory, to qualitatively explain the observed temperature and magnetic field dependence of the non-quantized thermal Hall conductivity $\kappa_{xy}$, with its origin lying in the Berry curvature of the magnon bands. The magnitude of the effect is however too small compared to the measurement by Czajka et al. [Nat. Mater. 22, 36-41 (2023)], even after scanning a broad range of model parameters so as to maximize $\kappa_{xy}/T$. Recent experiments suggest that phonons play an important role, which we show couple to the spins, endowing phonons with chirality. The resulting intrinsic contribution, from both magnons and phonons, is however still insufficient to explain the observed magnitude of the Hall signal. After careful analysis of the extrinsic phonon mechanisms, we use the recent experimental data on thermal transport in $\alpha$-RuCl$_3$ by Lefran\c{c}ois et al. [Phys. Rev. X 12, 021025 (2022)] to determine the phenomenological ratio of the extrinsic and intrinsic contributions $\eta\equiv \kappa_{xy}^{E}/\kappa_{xy}^{I}$. We find $\eta=1.2\pm 0.5$, which when combined with our computed intrinsic value, explains quantitavely both the magnitude and detailed temperature dependence of the experimental thermal Hall effect in $\alpha$-RuCl$_3$.Comment: 15 pages, 10 figures (including Supplementary Materials

Dynamic Peer Groups of Arbitrage Characteristics

We propose an asset pricing factor model constructed with semi-parametric characteristics based mispricing and factor loading functions. This model captures common movements of stock excess returns and includes a two-layer network of arbitrage returns in

AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition

The paper describes our proposed methodology for the six basic expression classification track of Affective Behavior Analysis in-the-wild (ABAW) Competition 2022. In Learing from Synthetic Data(LSD) task, facial expression recognition (FER) methods aim to learn the representation of expression from the artificially generated data and generalise to real data. Because of the ambiguous of the synthetic data and the objectivity of the facial Action Unit (AU), we resort to the AU information for performance boosting, and make contributions as follows. First, to adapt the model to synthetic scenarios, we use the knowledge from pre-trained large-scale face recognition data. Second, we propose a conceptually-new framework, termed as AU-Supervised Convolutional Vision Transformers (AU-CVT), which clearly improves the performance of FER by jointly training auxiliary datasets with AU or pseudo AU labels. Our AU-CVT achieved F1 score as $0.6863$, accuracy as $0.7433$ on the validation set. The source code of our work is publicly available online: https://github.com/msy1412/ABAW

Analyzing And Modelling Sewage Discharge Process Of Typical Area Using Time Series Analysis Method

This study is conducted to develop a mathematical model for typical sewage discharge area like residential area, commercial district and institutional area. An approach of time series analysis is applied to build the model involving model selection, parameter estimation, simulation and prediction. The description of sewage discharge process is divided into two parts: Periodic change and stationary random process. Periodic change process is simulated by harmonic analysis which composites a number of trigonometric function together. Stationary random process is described using Stationary time series including six steps: stationary test of the series; calculation of autocorrelation function and partial autocorrelation function for the series; identification of model type; determination of the model order; estimation of model parameters; verification of the model. In this paper daily variation process models for Sewage discharge of residential areas are built using this method. The numerical results show that the present method is effective and produce good agreements with the measured curve. Sewage discharge simulation of other areas like commercial area or institutional area could take the same way. This model could be used as a tool for uncertainty analysis of sewage discharge predicting. And the model also could be coupled with pipe flow model like SWMM to build sewage discharge analysis system in urban scale